{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import Libraries\n",
    "First of some preparation.We need to import python libraries containing the necessary functionality we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 有时候使用pandas包，对df表作处理时，常常会伴随警告出现，可以作如下处理\n",
    "# Ignore warning\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handles table-like data and matrices\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelling Algorithms\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modelling Helpers\n",
    "from sklearn.preprocessing import Imputer, Normalizer, scale\n",
    "from sklearn.cross_validation import train_test_split, StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualisation\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure visualisations\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')   # 这是个全局变量，定义绘图时的风格,ggplot是R语言中的一种常用的绘图风格\n",
    "sns.set_style('white')    # 设置绘图的美学样式\n",
    "pylab.rcParams['figure.figsize']=(8,6)   # 设置图表的大小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Setup helper Functions 设置一些辅助函数（主要是用于绘图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_histograms( df , variables , n_rows , n_cols ):\n",
    "    fig = plt.figure( figsize = ( 16 , 12 ) )\n",
    "    for i, var_name in enumerate( variables ):\n",
    "        ax=fig.add_subplot( n_rows , n_cols , i+1 )\n",
    "        df[ var_name ].hist( bins=10 , ax=ax )\n",
    "        ax.set_title( 'Skew: ' + str( round( float( df[ var_name ].skew() ) , ) ) ) # + ' ' + var_name ) #var_name+\" Distribution\")\n",
    "        ax.set_xticklabels( [] , visible=False )\n",
    "        ax.set_yticklabels( [] , visible=False )\n",
    "    fig.tight_layout()  # Improves appearance a bit.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_distribution( df , var , target , **kwargs ):\n",
    "    row = kwargs.get( 'row' , None )\n",
    "    col = kwargs.get( 'col' , None )\n",
    "    facet = sns.FacetGrid( df , hue=target , aspect=4 , row = row , col = col )\n",
    "    facet.map( sns.kdeplot , var , shade= True )\n",
    "    facet.set( xlim=( 0 , df[ var ].max() ) )\n",
    "    facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_categories( df , cat , target , **kwargs ):\n",
    "    row = kwargs.get( 'row' , None )\n",
    "    col = kwargs.get( 'col' , None )\n",
    "    facet = sns.FacetGrid( df , row = row , col = col )\n",
    "    facet.map( sns.barplot , cat , target )\n",
    "    facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_correlation_map( df ):\n",
    "    corr = titanic.corr()\n",
    "    _ , ax = plt.subplots( figsize =( 12 , 10 ) )\n",
    "    cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
    "    _ = sns.heatmap(\n",
    "        corr, \n",
    "        cmap = cmap,\n",
    "        square=True, \n",
    "        cbar_kws={ 'shrink' : .9 }, \n",
    "        ax=ax, \n",
    "        annot = True, \n",
    "        annot_kws = { 'fontsize' : 12 }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def describe_more( df ):\n",
    "    var = [] ; l = [] ; t = []\n",
    "    for x in df:\n",
    "        var.append( x )\n",
    "        l.append( len( pd.value_counts( df[ x ] ) ) )\n",
    "        t.append( df[ x ].dtypes )\n",
    "    levels = pd.DataFrame( { 'Variable' : var , 'Levels' : l , 'Datatype' : t } )\n",
    "    levels.sort_values( by = 'Levels' , inplace = True )\n",
    "    return levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_variable_importance( X , y ):\n",
    "    tree = DecisionTreeClassifier( random_state = 99 )\n",
    "    tree.fit( X , y )\n",
    "    plot_model_var_imp( tree , X , y )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_var_imp( model , X , y ):\n",
    "    imp = pd.DataFrame( \n",
    "        model.feature_importances_  , \n",
    "        columns = [ 'Importance' ] , \n",
    "        index = X.columns \n",
    "    )\n",
    "    imp = imp.sort_values( [ 'Importance' ] , ascending = True )\n",
    "    imp[ : 10 ].plot( kind = 'barh' )\n",
    "    print (model.score( X , y ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets: full: (1309, 12) titanic: (891, 12)\n"
     ]
    }
   ],
   "source": [
    "# get titantic & test csv files as a DataFrame\n",
    "train = pd.read_csv(\"all/train.csv\")\n",
    "test = pd.read_csv(\"all/test.csv\")\n",
    "full = train.append(test, ignore_index = True)\n",
    "titanic = full[:891]\n",
    "del train, test\n",
    "print('Datasets:','full:',full.shape,'titanic:',titanic.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Statistical summaries and visualisations\n",
    "To understand the data we are now going to consider some key facts about various variables including their relationship with the target variable,i.e.survival.\n",
    "We start by looking at a few lines of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "3  35.0  C123        S  53.1000   \n",
       "4  35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                           Allen, Mr. William Henry      0            5   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket  \n",
       "0       3    male      1       0.0         A/5 21171  \n",
       "1       1  female      1       1.0          PC 17599  \n",
       "2       3  female      0       1.0  STON/O2. 3101282  \n",
       "3       1  female      1       1.0            113803  \n",
       "4       3    male      0       0.0            373450  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.1 Next have a look at some key information about the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.526497</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.125000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Fare       Parch  PassengerId      Pclass  \\\n",
       "count  714.000000  891.000000  891.000000   891.000000  891.000000   \n",
       "mean    29.699118   32.204208    0.381594   446.000000    2.308642   \n",
       "std     14.526497   49.693429    0.806057   257.353842    0.836071   \n",
       "min      0.420000    0.000000    0.000000     1.000000    1.000000   \n",
       "25%     20.125000    7.910400    0.000000   223.500000    2.000000   \n",
       "50%     28.000000   14.454200    0.000000   446.000000    3.000000   \n",
       "75%     38.000000   31.000000    0.000000   668.500000    3.000000   \n",
       "max     80.000000  512.329200    6.000000   891.000000    3.000000   \n",
       "\n",
       "            SibSp    Survived  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.523008    0.383838  \n",
       "std      1.102743    0.486592  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    0.000000  \n",
       "50%      0.000000    0.000000  \n",
       "75%      1.000000    1.000000  \n",
       "max      8.000000    1.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.2 A heat map of correlation may give us a understanding of which variables are important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correlation_map(titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.3 Let's further explore the relationship between the features and survival of passengers\n",
    "\n",
    "We start by looking at the relationship between age and survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 918.75x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(titanic,var='Age',target='Survived',row='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "look at the relationship between Fare and survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 918.75x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distribution(titanic,var='Fare',target='Survived',row='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.4 Embarked\n",
    "\n",
    "We can also look at categorical variables like Embarked and their relationship with survival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 224x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot survival rate by Embarked\n",
    "plot_categories(titanic, cat = 'Embarked', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 224x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot survival rate by Sex\n",
    "plot_categories(titanic, cat = 'Sex', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EREgdo8M1NDRgyZIlCAsLQ2BgII4dOyZ1pA7T1NSEZcuWITQ0FOHh4bh69arUkVrEy9n8Q3JyMg4dOgQrKyupo3S4Q4cOoUePHoiPj0d1dTWmTp1qMtd5eHA87u7du5Gfn4/169frDiU0JtzC/UOfPn2QkJAgdQxB+Pn5YeHChbr7ZmZmEqbpWOPHj0dsbCwA4MaNG3B0dJQ4Ucu4hfuHSZMm4dq1a1LHEISNjQ2A+4fdLViwAIsWLZI4UccyNzfH+++/j++++w6bNm2SOk6LuIXrYm7evInIyEhMmTIFAQEBUsfpcHFxcTh69ChWrFgBlUoldZxHsHBdyO3btzFr1iwsWbIEgYGBUsfpUBkZGfj0008BAFZWVpDJZEb5kZmF60KSkpJQU1ODLVu2ICIiAhEREairq5M6VoeYOHEiLly4gPDwcMyePRvLly+HpaWl1LEewUO7iETELRyRiFg4IhGxcEQiYuGIRMTCEYmIR5p0QteuXcOECROajaXQarWIjIxsdf9aeno6jh49qttXRdJg4Tqp7t274+DBg7r7t27dwuTJk+Hp6Ql3d3cJk5E+LJyJcHJygqurK65cuYKTJ0/iwIEDMDc3h6urKxQKRbNli4qKEB8fD7VajcrKSgwfPhzr1q1DY2MjYmNjUVBQAAsLCzz99NNYv349LC0tW3z8wbGZZDgWzkQUFhbi6tWruHfvHtLT05GWlgYHBwesX78eO3bsgJOTk27ZL7/8EgsWLMArr7yCu3fvwtfXF+fOnUNdXR1Onz6Nr7/+GjKZDPHx8SgtLYVGo2nxcW9vbwn/4s6Jheuk6urqMGXKFAD3T77s2bMn4uPjkZubCz8/Pzg4OAAAli1bBuD+d7gHFAoFcnJykJSUhMuXL6O+vh4qlQru7u4wMzNDUFAQRowYgUmTJsHLyws1NTUtPk5tx8J1Uv/8DvfAjz/+CJlMprtfU1ODmpqaZsvMmDEDbm5uGDlyJF599VUUFxdDq9XC3t4eBw8eREFBAfLy8rBo0SLMnj0b4eHhrT5ObcPCmZjhw4djw4YNiIqKgq2tLRISEqDVauHh4QHgfgHPnj2L5ORkODg4ID8/H1evXoVGo0F2djY+//xzpKSkYMiQIdBqtTh37lyrj1PbsXAmZvTo0bh06RKmT58OAOjXrx9iY2Px7bffAgDs7e3x1ltvYerUqbC2toaTkxO8vb1RUVGBoKAg5OTkYPLkybC2toaDgwNiY2Ph7Ozc4uPUdjxbgEhEPNKESEQsHJGIWDgiEbFwRCJi4YhExMIRiYiFIxLR/wFKSJ5F5uVKngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 224x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot survival rate by Pclass\n",
    "plot_categories(titanic, cat = 'Pclass', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 224x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot survival rate by SibSp\n",
    "plot_categories(titanic, cat = 'SibSp', target = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 224x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot survival rate by Parch\n",
    "plot_categories(titanic, cat = 'Parch', target = 'Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Data Preparation\n",
    "\n",
    "3.1 Categorical variables need to be transformed to numeric variables\n",
    "\n",
    "The variables Embarked,Pclass and Sex are treated as categorical variables.Some of our model algorithms can only handle numeric values and so we need to create a new variable(dummy variable) for every unique value of the categorical variables.\n",
    "\n",
    "This variable will have a value 1 if the row has a particular value and a value 0 if not.Sex is a dichotomy and will be encoded as one binary variable(0 or 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform Sex into binary values 0 and 1\n",
    "sex = pd.Series(np.where(full.Sex == 'male', 1, 0), name = 'Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Embarked_C  Embarked_Q  Embarked_S\n",
       "0           0           0           1\n",
       "1           1           0           0\n",
       "2           0           0           1\n",
       "3           0           0           1\n",
       "4           0           0           1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new variable for every unique value of Embarked\n",
    "embarked = pd.get_dummies(full.Embarked, prefix='Embarked')\n",
    "embarked.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass_1  Pclass_2  Pclass_3\n",
       "0         0         0         1\n",
       "1         1         0         0\n",
       "2         0         0         1\n",
       "3         1         0         0\n",
       "4         0         0         1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new variable for every unique value of Embarked\n",
    "pclass = pd.get_dummies(full.Pclass, prefix='Pclass')\n",
    "pclass.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Fill missing values in variables\n",
    "\n",
    "Most machine learning alghorims requires to have values in order to use it for training the model.The simplest method is to fill missing values with the average of the variable across all observations in the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age     Fare\n",
       "0  22.0   7.2500\n",
       "1  38.0  71.2833\n",
       "2  26.0   7.9250\n",
       "3  35.0  53.1000\n",
       "4  35.0   8.0500"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataset\n",
    "imputed = pd.DataFrame()\n",
    "# Fill missing values of Age with the average of Age(mean)\n",
    "imputed['Age'] = full.Age.fillna(full.Age.mean())\n",
    "# Fill missing values of Fare with the average of Fare(mean)\n",
    "imputed['Fare'] = full.Fare.fillna(full.Fare.mean())\n",
    "imputed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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